Cybersecurity is the security cornerstone of digital transformation of the power grid and construction of new power systems. The traditional network security situation quantification method only analyzes from the perspective of network performance, ignoring the impact of various power application services on the security situation, so the quantification results cannot fully reflect the power information network risk state. This study proposes a method for quantifying security situation of the power information network based on the evolutionary neural network. First, the security posture system architecture is designed by analyzing the business characteristics of power information network applications. Second, combining the importance of power application business, the spatial element index system of coupled interconnection is established from three dimensions of network reliability, threat, and vulnerability. Then, the BP neural network optimized by the genetic evolutionary algorithm is incorporated into the element index calculation process, and the quantitative model of security posture of the power information network based on the evolutionary neural network is constructed. Finally, a simulation experiment environment is built according to a power sector network topology, and the effectiveness and robustness of the method proposed in the study are verified.

Today's digital economy is blurring the boundaries between computer science and economics -- in Silicon Valley, on Wall Street, and increasingly on university campuses. Yale undergraduates interested in both fields can pursue the Computer Science and Economics (CSEC) interdepartmental degree program, which launched in fall 2019, with coursework covering topics such as machine learning and computational finance. Philipp Strack, CSEC's inaugural director of undergraduate studies, is comfortable straddling multiple disciplines. With an academic background in economics and mathematics, his research reflects this broad and interdisciplinary outlook -- ranging from behavioral economics and neuroscience to auction design, market design, optimization, and pure probability theory. Strack, an associate professor of economics in the Faculty of Arts and Sciences, recently spoke to YaleNews about the real-world implications of this work, what the CSEC program offers students, and how it bridges these critical fields.

Data science is one of the most well-payed jobs in the contemporary market. It is even considered as the hottest job of the 21st century. Data science has been a game-changer across every industry. With high-level digitization of processes, the generation of data is at peak and thus data science technology and tools are deployed to drive more productivity across organizations. This tech-field as a whole has a bunch of perks to provide including technologies for Big Data, Data Mining, Machine Learning, Data Analysis, and Data Analytics.

In an era of data driven digital transformation, a customer driven business strategy is essential for success. In the motion picture industry, movie exhibitors must compete to win share of consumers entertainment time (and wallet) against digital entertainment alternatives offered by mammoth sized, digital focused, competitors like Netflix, Amazon and Disney [1]. Customer loyalty, point-of-sale and digital payment p latforms produce rich insights that can leveraged to inform business operations and automate the decision-making pr ocess, effectively enabling movie exhibitors to compete using analytics and artificial intelligence. This study presen ts a new, customer driven, quantitative approach to movie scheduling that can be utilized by movie exhibitors to increase attendance and market share. The role of the exhibitor is to show films that are produced by movie st udios (see [2] for more details on the roles of the stakeholders in the movie industry). Exhibitors do not have d ecision making authority over the movies that are produced by the studios.

There is still no Wikipedia explanation on Social Data Science, not that it would make it established as a field, but it is more of a side note in these beginnings. The last few days I have been considering how to put together a program that would unite people from different backgrounds to explore the topic of artificial intelligence. Doing so led me into the path of exploring firstly four different modules relating to: AI, science and technology studies (STS) and computer science ethics. To begin with I would say there does not seem to be very fruitful to mesh them together without a thought or regard for each field and their historical connotations, literature, personas and so on. However new practitioners or researchers who increasingly combine an understanding or expertise in social science with the performative skills in programming is edging into the conceived emerging field of social data science. I find this worthy of further exploration.

The European Banking Authority (EBA) has conducted a series of meetings over the past few months to explore the state of the art of Artificial Intelligence (AI) adoption in the banking sector and identify the best regulatory approach for validation processes. These conversations bring to the surface important aspects regarding transparency of the algorithms, robustness of the processes, security of the applications, ethics of the decision-making. I am not new to discussing similar issues with regulators, given my professional risk management background to validate first internal models in the late 1990s. I was therefore pleased to join the debate and contribute with my experience and the IBM point of view. The banking industry experienced a period of "quantitative exuberance" in the 1990s and early 2000s.

Truly revolutionary political transformations are naturally of great interest to historians, and the French Revolution at the end of the 18th century is widely regarded as one of the most influential, serving as a model for building other European democracies. A paper published last summer in the Proceedings of the National Academy of Sciences, offers new insight into how the members of the first National Constituent Assembly hammered out the details of this new type of governance. Specifically, rhetorical innovations by key influential figures (like Robespierre) played a critical role in persuading others to accept what were, at the time, audacious principles of governance, according to co-author Simon DeDeo, a former physicist who now applies mathematical techniques to the study of historical and current cultural phenomena. And the cutting-edge machine learning methods he developed to reach that conclusion are now being employed by other scholars of history and literature. As more and more archives are digitized, scholars are applying various analytical tools to those rich datasets, such as Google N-gram, Bookworm, and WordNet.

Truly revolutionary political transformations are naturally of great interest to historians, and the French Revolution at the end of the 18th century is widely regarded as one of the most influential, serving as a model for building other European democracies. A paper published last summer in the Proceedings of the National Academy of Sciences, offers new insight into how the members of the first National Constituent Assembly hammered out the details of this new type of governance. Specifically, rhetorical innovations by key influential figures (like Robespierre) played a critical role in persuading others to accept what were, at the time, audacious principles of governance, according to co-author Simon DeDeo, a former physicist who now applies mathematical techniques to the study of historical and current cultural phenomena. And the cutting-edge machine learning methods he developed to reach that conclusion are now being employed by other scholars of history and literature. As more and more archives are digitized, scholars are applying various analytical tools to those rich datasets, such as Google N-gram, Bookworm, and WordNet.

Every day, financial markets and global economies produce a flood of data. As a result, stock traders now have more information about more industries and sectors than ever before. That deluge, combined with the rise of cloud technology, has inspired hedge funds to develop new quantitative strategies that they hope can generate greater returns than the experience and judgement of their own staff. At the Future of Fintech conference hosted by research company CB Insights in New York City, three hedge fund insiders discussed the latest developments in quantitative trading. A session on Tuesday featured Christina Qi, the co-founder of a high-frequency trading firm called Domeyard LP; Jonathan Larkin, an executive from Quantopian, a hedge fund taking a data-driven systematic approach; and Andy Weissman of Union Square Ventures, a venture capital firm that has invested in an autonomous hedge fund. Many of the world's largest hedge funds already rely on powerful computing infrastructure and quantitative methods--whether that's high-frequency trading, incorporating machine learning, or applying data science--to make trades.

Assessing generative models is not an easy task. Generative models should synthesize graphs which are not replicates of real networks but show topological features similar to real graphs. We introduce an approach for assessing graph generative models using graph classifiers. The inability of an established graph classifier for distinguishing real and synthesized graphs could be considered as a performance measurement for graph generators.